A systematic review of the clinical use of <i>Withania somnifera</i> (Ashwagandha) to ameliorate cognitive dysfunction
Bibliographic record
Abstract
Many developed countries are experiencing a rapidly "greying" population, and cognitive decline is common in the elderly. There is no cure for dementia, and pharmacotherapy options to treat cognitive dysfunction provide limited symptomatic improvements. Withania somnifera (Ashwagandha), a popular herb highly valued in Ayurvedic medicine, has often been used to aid memory and cognition. This systematic review thus aimed to evaluate the clinical evidence base and investigate the potential role of W. somnifera in managing cognitive dysfunction. Using the following keywords [withania somnifera OR indian ginseng OR Ashwagandha OR winter cherry] AND [brain OR cognit* OR mental OR dementia OR memory], a comprehensive search of PubMed, EMBASE, Medline, PsycINFO and Clinicaltrials.gov databases found five clinical studies that met the study's eligibility criteria. Overall, there is some early clinical evidence, in the form of randomized, placebo-controlled, double-blind trials, to support the cognitive benefits of W. somnifera supplementation. However, a rather heterogeneous study population was sampled, including older adults with mild cognitive impairment and adults with schizophrenia, schizoaffective disorder, or bipolar disorder. In most instances, W. somnifera extract improved performance on cognitive tasks, executive function, attention, and reaction time. It also appears to be well tolerated, with good adherence and minimal side effects.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".